Author
Articles by Gilad Gal
Principal Product Manager I, Elastic
Elastic Platform 8.15: Native vector database now supports bit vectors, SIMD acceleration, and simple config
Elasticsearch native vector database now supports bit vectors, SIMD acceleration, and int4 quantization. The new semantic_text field type makes generating embeddings simple. ES|QL has new query capabilities and an expanded UI in Kibana Discover.
Elastic Platform 8.14: ES|QL GA, encryption at rest, and vector search optimizations
Elastic Platform 8.14 includes ES|QL GA, the use of AI for pattern recognition in logs, API key based security model for remote clusters, encryption at rest with KMS keys, retrievers, several vector optimizations, and vector quantization by default.
Elasticsearch and Kibana 8.13: Simplified kNN and improved query parallelization
Elasticsearch and Kibana 8.13 deliver simplified kNN, improved query parallelization plus improvements to ES|QL, anomaly detection, and new improved health indicators. Also announcing the Elastic Integration Filter and GA of Kafka for Elastic Agent.
Elastic Stack 8.11: Introducing a new powerful query language, ES|QL
Elastic Stack 8.11 introduces an advanced query language known as ES|QL in the Discover application, making data exploration more straightforward and user friendly. The Elastic Learned Sparse EncodeR (ELSER) is now generally available.
Elastic Stack 8.10: Simpler cross-cluster search and authentication, and more
Simplify configuring cross-cluster search, execute vector search faster, detect data drifts and log rate dips, stream Elastic Agent to Kafka, and authenticate Webhook connector using third-party security certificates with Elastic Stack 8.10.
Elastic Search 8.9: Hybrid search with RRF, faster vector search, and public-facing search endpoints
Elastic Search 8.9 brings improvements to vector search and ingestion and presents hybrid search with RRF to combine vector, keyword, and semantic techniques. Public-facing search endpoints for indices are now available with search applications beta.
Introducing Elastic Learned Sparse Encoder: Elastic’s AI model for semantic search
Elastic Learned Sparse Encoder is an AI model for high relevance semantic search across domains. As a sparse vector model, it expands the query with terms that don't exist in the query itself, delivering superior relevance without domain adaptation.
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